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Comment by Traster | Hacker News Reader

I think this headline is bad. When you read the abstract you find that what this is really saying is that there are essentially no studies that are fit for purpose to evaluate AI systems for breast screening in general and those that are the closest to being suitable show the worst results. That's the real take-away here - we're not even at the point where we're collecting good data about AI accuracy, let alone actually producing accurate AI.

I would suggest these two things might be linked - the only way you get publishable results is by failing to do rigorous studies.

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We've since changed the title. The submitted title ("94% of 36 AI systems evaluated were less accurate than a single radiologist") broke the site guidelines by editorializing. Submitters: please don't do that.

"Please use the original title, unless it is misleading or linkbait; don't editorialize." - https://news.ycombinator.com/newsguidelines.html

You can get away with publishing models with worse accuracies if you can pitch a more fair evaluation scheme, but it's way harder, and you don't get to do flashy presentations where you state you're SotA.

I hope I don't offend any other people in the field, but I think historically fields like histology/radiology/MRI screening via AI have been subject to less scrutiny by virtue of their multi-disciplinary nature... particularly in the past, it was hard to find reviewers that both (A) understood the biological/clinical validation (B) the technical validation.

I think things have drastically changed over even the last 5 years, which is why you're more likely to see headlines like these. We have a lot of discussions like these during our lab's journal clubs. The optimists among us (e.g. myself) argue that this opens up a lot of opportunities whereby more fair validations/benchmarks allows us to compete with "SotA" methods that are actually fragile and easy to surpass on even ground. More pessimistic members are quick to note that it's far harder to introduce new, fairer validations and that reporting lower metrics is far less buzz worthy (all true points).

This reminds me of a fundamental error that was recently found in methods that tried to predict protein-protein interactions. It was found that the train/test/validation method used by ostensibly every paper was leaking huge amounts of data [0]. When we plugged that leak, we saw much more modest metrics, but focusing on regularisation allowed us to beat the competition [1].

The kicker? The information leak were identified in 2012, yet you'll see papers written every year that have the same leak and report >90% accuracies, and >0.9 ROCs.

[0] https://www.nature.com/articles/nmeth.2259

[1] https://www.biorxiv.org/content/10.1101/2021.08.13.456309v1

The headline communicates that AI has effectively failed so far. That's a good thing to communicate.

Also publications are not what determines if AI get deployed in clinical practice. That's the job of the FDA and million of dollars spent on validation like clinical trials and quality management systems.

I might be misreading your comment, but, are you saying that this study is not rigorous? There's a bit of a disingenuous tone in that assertion.

No, this paper is a review of other studies, and what I'm saying is it finds that there are quite a few problems with the studies they're reviewing - they point out that the smaller studies that show stronger results are actually less likely to be generalizable, and the studies that are broader show weaker results but even then they still have flaws in the method. There is quite a good table (figure 2) that shows how they rate their "concerns" for a number of metrics, and it's a sea of red where they have lots of concerns.

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